Risk Measures Research Articles

The Diversification Benefits of Foreign Real Estate: Evidence from 40 Years of Data

We investigate the potential of global real estate to improve the long-term performance of a US equity portfolio, utilizing a recent dataset of 40 years’ worth of US stocks, US real estate, 13 foreign stock markets, and 13 foreign real estate markets across diverse regions. Despite a modest performance in terms of risk and return, foreign real estate has consistently lower correlations with US stocks compared to foreign equities. Rolling correlation analysis indicates that foreign real estate markets remain relatively segmented compared to foreign equity, despite increasing financial market correlations over time. Efficient frontier analysis demonstrates that portfolios including foreign real estate consistently outperform those limited to US stocks and US real estate or those including foreign stocks, indicating the importance of foreign real estate in optimizing portfolio performance. Subperiod analysis reveals that foreign real estate retains its diversification benefits even in the latter, more integrated period. Our results are robust when using Conditional Value-at-Risk as a measure of risk. Overall, our findings highlight the persistent diversification benefits and superior risk-adjusted returns from incorporating foreign real estate into US equity portfolios.

Systemic Financial Risk Forecasting with Decomposition–Clustering-Ensemble Learning Approach: Evidence from China

Establishing a scientifically effective systemic financial risk early warning model is of great significance for prudently mitigating systemic financial risks and enhancing the efficiency of financial supervision. Based on the measurement of systemic financial risk and the network sentiment index of 47 financial institutions, this study adopted the “decomposition–reconstruction–integration” approach, utilizing techniques such as extreme-point symmetric empirical mode decomposition (ESMD), empirical mode decomposition (EMD), variational mode decomposition (VMD), hierarchical clustering, fast independent component analysis (FastICA), attention mechanism, bidirectional long short-term memory neural network (BiLSTM), support vector regression (SVR), and their combination, to construct a systemic financial risk prediction model. The empirical results demonstrate that decomposing and reconstructing relevant indicators before predicting systemic financial risks can enhance prediction accuracy. Among the proposed models, the ESMD-HFastICA-BiLSTM-Attention model exhibits superior performance in systemic financial risk early warning.

Validity and reliability of the Violence Risk Scale-Sexual Offense version in a community sexual offense outpatient setting.

The present study examined the convergent, structural, and predictive properties of Violence Risk Scale-Sexual Offense version (VRS-SO) scores in a sample of 200 men on community supervision for sexual offenses, attending forensic community outpatient services and followed up an average 8.6 years. The VRS-SO and two additional dynamic sexual recidivism risk measures-STABLE 2007 and Sex Offender Treatment Intervention and Progress Scale (SOTIPS)-were coded archivally from clinic files; Static-99R ratings were extracted. Recidivism data were captured from Royal Canadian Mounted Police records. VRS-SO static, dynamic, and total scores demonstrated expected patterns of convergence with total and subscale scores of the risk measures. Moreover, a confirmatory factor analysis of the VRS-SO dynamic item scores demonstrated acceptable model fit for a correlated three-factor solution consistent with prior confirmatory factor analyses. Discrimination analyses demonstrated that VRS-SO dynamic and total scores and STABLE 2007 scores had large prediction effects for 5-year sexual recidivism (area under the curves [AUCs] = .71-.72) while SOTIPS had a medium effect for this outcome (AUC = .67); the measures yielded medium to large effects for nonsexual recidivism. Cox regression survival analyses demonstrated that VRS-SO dynamic, Sexual Deviance factor, and SOTIPS scores each incrementally predicted sexual recidivism controlling for Static-99R or VRS-SO static factor scores. VRS-SO calibration analyses demonstrated that expected or predicted 5-year sexual recidivism rates showed generally close correspondence to the rates predicted or observed in the present community sample. Results support the psychometric properties of the VRS-SO, a sexual violence risk assessment and treatment planning measure, to a community outpatient sample. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Risk Measures’ Duality on Ordered Linear Spaces

The aim of this paper is to provide a dual representation of convex and coherent risk measures in partially ordered linear spaces with respect to the algebraic dual space. An algebraic robust representation is deduced by weak separation of convex sets by functionals, which are assumed to be only linear; thus, our framework does not require any topological structure of the underlying spaces, and our robust representations are found without any continuity requirement for the risk measures. We also use such extensions to the representation of acceptability indices.

Non–High-Density Lipoprotein Cholesterol Levels From Childhood to Adulthood and Cardiovascular Disease Events

Elevated non-high-density lipoprotein cholesterol (non-HDL-C; a recommended measure of lipid-related cardiovascular risk) is common in children and increases risk of adult cardiovascular disease (CVD). Whether resolution of elevated childhood non-HDL-C levels by adulthood is associated with reduced risk of clinical CVD events is unknown. To examine the associations of non-HDL-C status between childhood and adulthood with incident CVD events. Individual participant data from 6 prospective cohorts of children (mean age at baseline, 10.7 years) in the US and Finland. Recruitment took place between 1970 and 1996, with a final follow-up in 2019. Child (age 3-19 years) and adult (age 20-40 years) non-HDL-C age- and sex-specific z scores and categories according to clinical guideline-recommended cutoffs for dyslipidemia. Incident fatal and nonfatal CVD events adjudicated by medical records. Over a mean length of follow-up of 8.9 years after age 40 years, 147 CVD events occurred among 5121 participants (60% women; 15% Black). Both childhood and adult non-HDL-C levels were associated with increased risk of CVD events (hazard ratio [HR], 1.42 [95% CI, 1.18-1.70] and HR, 1.50 [95% CI, 1.26-1.78] for a 1-unit increase in z score, respectively), but the association for childhood non-HDL-C was reduced when adjusted for adult levels (HR, 1.12 [95% CI, 0.89-1.41]). A complementary analysis showed that both childhood non-HDL-C levels and the change between childhood and adulthood were independently associated with the outcome, suggesting that from a preventive perspective, both childhood non-HDL-C levels and the change into adulthood are informative. Compared with those whose non-HDL-C levels remained within the guideline-recommended range in childhood and adulthood, participants who had incident non-HDL-C dyslipidemia from childhood to adulthood and those with persistent dyslipidemia had increased risks of CVD events (HR, 2.17 [95% CI, 1.00-4.69] and HR, 5.17 [95% CI, 2.80-9.56], respectively). Individuals who had dyslipidemic non-HDL-C in childhood but whose non-HDL-C levels were within the guideline-recommended range in adulthood did not have a significantly increased risk (HR, 1.13 [95% CI, 0.50-2.56]). Individuals with persistent non-HDL-C dyslipidemia from childhood to adulthood had an increased risk of CVD events, but those in whom dyslipidemic non-HDL-C levels resolve by adulthood have similar risk to individuals who were never dyslipidemic. These findings suggest that interventions to prevent and reduce elevated childhood non-HDL-C levels may help prevent premature CVD.

The mediating effect of bank risk on the relationship between diversification strategies and discretionary loan loss provisions

Purpose This study aims to investigate the effect of income and asset diversification on earnings management using discretionary loan loss provisions (LLP) in banks, and the role of risk level in mediating this effect. Design/methodology/approach A sample of banks operating in Middle East and North Africa countries was used to test the mediation model of Baron and Kenny (1986) with different measures of diversification and risk. Findings The results show that bank income and asset diversification have unique and combined effects on earnings management. The results also support the idea that a risk-mediating effect contributes to explaining this relationship among banks. Specifically, bank diversification strategies positively affect LLP-based earnings management by increasing bank risk. This result is relevant for conventional banks. However, only a direct and positive effect of diversification strategies on LLP-based earnings management can be observed in Islamic banks, and the indirect effect is not supported. Originality/value This study extends previous research by examining the unique and combined effects of income and asset diversification strategies on earnings management in the banking sector. Specifically, it provides new evidence that diversification strategies increase LLP-based earnings management, both directly and indirectly, through bank risk.

Self-Rated Health in Middle Age and Risk of Hospitalizations and Death: Recurrent Event Analysis of the ARIC Study.

Self-rated health is a simple measure that may identify individuals who are at a higher risk for hospitalization or death. To quantify the association between a single measure of self-rated health and future risk of recurrent hospitalizations or death. Atherosclerosis Risk in Communities (ARIC) study, a community-based prospective cohort study of middle-aged men and women with follow-up beginning from 1987 to 1989. We quantified the associations between initial self-rated health with risk of recurrent hospitalizations and of death using a recurrent events survival model that allowed for dependency between the rates of hospitalization and hazards of death, adjusted for demographic and clinical factors. Of the 14,937 ARIC cohort individuals with available self-rated health and covariate information, 34% of individuals reported "excellent" health, 47% "good," 16% "fair," and 3% "poor" at study baseline. After a median follow-up of 27.7years, 1955 (39%), 3569 (51%), 1626 (67%), and 402 (83%) individuals with "excellent," "good," "fair," and "poor" health, respectively, had died. After adjusting for demographic factors and medical history, a less favorable self-rated health status was associated with increased rates of hospitalization and death. As compared to those reporting "excellent" health, adults with "good," "fair," and "poor" health had 1.22 (1.07 to 1.40), 2.01 (1.63 to 2.47), and 3.13 (2.39 to 4.09) times the rate of hospitalizations, respectively. The hazards of death also increased with worsening categories of self-rated health, with "good," "fair," and "poor" health individuals experiencing 1.30 (1.12 to 1.51), 2.15 (1.71 to 2.69), and 3.40 (2.54 to 4.56) times the hazard of death compared to "excellent," respectively. Even after adjusting for demographic and clinical factors, having a less favorable response on a single measure of self-rated health taken in middle age is a potent marker of future hospitalizations and death.

The Positive and Negative Suicidal Ideation Inventory among Portuguese Adolescents: Factor Structure and Gender Invariance

Suicide worldwide is an issue that needs to be addressed, and adolescents are an at-risk group. Assessing suicidal ideation is central to tackling the issue of suicide. The Positive and Negative Suicidal Ideation inventory is a widely validated measure of suicidal ideation, and yet, very little is known about its invariance across various groups. The present study aimed to adapt and test the PANSI’s structure in a Portuguese sample while testing its gender invariance. A total of 750 middle and high school students were recruited for the study, and data were collected on various suicide risk and protective factors, including the Portuguese-translated PANSI. Data were put through exploratory and confirmatory factor analysis. Kaiser’s criterion and scree plot both extracted two factors (64.10% variance explained). Confirmatory factor analysis also supported the PANSI’s structure (TLI = 0.943). The PANSI showed good reliability (α ≥ 0.83) and good construct and discriminative validity. The PANSI also exhibited scalar, but not strict, invariance. Overall, these results were similar to previous versions of this scale. The PANSI is a reliable measure of suicide risk among Portuguese adolescents. Future studies should further replicate these results in other cultures and expand on them by testing for invariance across other demographic variables.

Associations between monitor-independent movement summary (MIMS) and fall risk appraisal combining fear of falling and physiological fall risk in community-dwelling older adults

Introduction: Fall Risk Appraisal (FRA), a process that integrates perceived and objective fall risk measures, serves as a crucial component for understanding the incongruence between fear of falling (FOF) and physiological fall risk in older adults. Despite its importance, scant research has been undertaken to investigate how habitual physical activity (PA) levels, quantified in Monitor-Independent Movement Summary (MIMS), vary across FRA categories. MIMS is a device-independent acceleration summary metric that helps standardize data analysis across studies by accounting for discrepancies in raw data among research-grade and consumer devices.Objective: This cross-sectional study explores the associations between MIMS (volume and intensity) and FRA in a sample of older adults in the United States.Methods: We assessed FOF (Short Falls Efficacy Scale-International), physiological fall risk (balance: BTrackS Balance, leg strength: 30-s sit-to-stand test) and 7-day free-living PA (ActiGraph GT9X) in 178 community-dwelling older adults. PA volume was summarized as average daily MIMS (MIMS/day). PA intensity was calculated as peak 30-min MIMS (average of highest 30 non-consecutive MIMS minutes/day), representing a PA index of higher-intensity epochs. FRA categorized participants into following four groups: Rational (low FOF-low physiological fall risk), Irrational (high FOF-low physiological fall risk), Incongruent (low FOF-high physiological fall risk) and Congruent (high FOF-high physiological fall risk).Results: Compared to rational group, average MIMS/day and peak 30-min MIMS were, respectively, 15.8% (p = .025) and 14.0% (p = .004) lower in irrational group, and 16.6% (p = .013) and 17.5% (p < .001) lower in congruent group. No significant differences were detected between incongruent and rational groups. Multiple regression analyses showed that, after adjusting for age, gender, and BMI (reference: rational), only irrational FRA was significantly associated with lower PA volume (β = −1,452.8 MIMS/day, p = .034); whereas irrational and congruent FRAs were significantly associated with lower “peak PA intensity” (irrational: β = −5.40 MIMS/day, p = .007; congruent: β = −5.43 MIMS/day, p = .004).Conclusion: These findings highlight that FOF is a significant barrier for older adults to participate in high-intensity PA, regardless of their balance and strength. Therefore, PA programs for older adults should develop tailored intervention strategies (cognitive reframing, balance and strength exercises, or both) based on an individual’s FOF and physiological fall risk.

Real Estate Development Feasibility and Hurdle Rate Selection

Real estate developers typically assess potential projects using feasibility analyses and industry-standard heuristics, which include capital costs, return on costs, and a subjective risk measure. This study explores real estate developers’ decision-making practices in selecting hurdle rates and common feasibility analysis techniques, surveying 225 Australian and New Zealand developers. The main findings are that most developers use specific ‘go/no-go’ hurdle rate mechanisms irrespective of primary real estate type, with the majority using margin on development cost (MDC) or internal rate of return (IRR); the boundaries between traditional speculative development and real estate investment through the use of securitization methods have become blurred; many developers use both quantitative metrics, with qualitative methods and specific structural checks to manage the risks involved; and the two most frequent methods of determining site value prior to acquisition are the residual land value and discounted cash flow methods. Most place a heavy reliance on industry-accepted heuristics and do not have a predetermined process and method for altering or adapting the chosen hurdle rates and benchmarks. This research provides a contribution to property development practice from the Antipodean perspective which until now has been more focused on the UK view, enabling more generalized application internationally.

Revolutionizing Health Management: Developing a Wearable Device for Real-time Heart Rate Measurement and Prediction of Hypertension Risks

This research paper introduces SyncFit, a wearable gadget coupled with a web platform for instant heart rate monitoring and hypertension risk forecasting. The gadget employs a Python-based random forest algorithm to scrutinize heart rate data and anticipate abnormal rates signaling hypertension risk. The web platform, constructed with Streamlit and Firebase, grants users access to their heart rate analysis and hypertension risk evaluation. The research showcases SyncFit's capability to transform health management by enabling individuals to proactively monitor their health and predict risks, thereby facilitating optimization of their wellness journey. SyncFit enhances user experience with a range of features beyond its core functions, ensuring effortless integration into daily routines. Its sleek, ergonomic design prioritizes comfort, enabling seamless wear throughout the day. Additionally, its intuitive interface and user-friendly controls facilitate easy navigation and customization, catering to diverse user preferences. SyncFit's robust construction and advanced tech establish a new benchmark for wearable health monitors, blending style and functionality to empower users in their wellness journey. SyncFit commits to constant improvement and innovation, with ongoing R&D aimed at expanding capabilities and addressing emerging health challenges. Future versions will integrate more sensors and advanced analytics, offering a deeper understanding of health. Integration with AI and cloud tech will revolutionize personalized health management, making preventive care proactive, empowering individuals to optimize well-being. Key Words: wearable device, heart rate, hypertension risk, web application, machine learning, artificial intelligence, streamlit.

Quantum Monte Carlo simulations for financial risk analytics: scenario generation for equity, rate, and credit risk factors

Monte Carlo (MC) simulations are widely used in financial risk management, from estimating value-at-risk (VaR) to pricing over-the-counter derivatives. However, they come at a significant computational cost due to the number of scenarios required for convergence. If a probability distribution is available, Quantum Amplitude Estimation (QAE) algorithms can provide a quadratic speed-up in measuring its properties as compared to their classical counterparts. Recent studies have explored the calculation of common risk measures and the optimisation of QAE algorithms by initialising the input quantum states with pre-computed probability distributions. If such distributions are not available in closed form, however, they need to be generated numerically, and the associated computational cost may limit the quantum advantage. In this paper, we bypass this challenge by incorporating scenario generation – i.e. simulation of the risk factor evolution over time to generate probability distributions – into the quantum computation; we refer to this process as Quantum MC (QMC) simulations. Specifically, we assemble quantum circuits that implement stochastic models for equity (geometric Brownian motion), interest rate (mean-reversion models), and credit (structural, reduced-form, and rating migration credit models) risk factors. We then integrate these models with QAE to provide end-to-end examples for both market and credit risk use cases.

An extreme worst-case risk measure by expectile

Abstract Expectiles have received increasing attention as a risk measure in risk management because of their coherency and elicitability at the level $\alpha\geq1/2$ . With a view to practical risk assessments, this paper delves into the worst-case expectile, where only partial information on the underlying distribution is available and there is no closed-form representation. We explore the asymptotic behavior of the worst-case expectile on two specified ambiguity sets: one is through the Wasserstein distance from a reference distribution and transforms this problem into a convex optimization problem via the well-known Kusuoka representation, and the other is induced by higher moment constraints. We obtain precise results in some special cases; nevertheless, there are no unified closed-form solutions. We aim to fully characterize the extreme behaviors; that is, we pursue an approximate solution as the level $\alpha $ tends to 1, which is aesthetically pleasing. As an application of our technique, we investigate the ambiguity set induced by higher moment conditions. Finally, we compare our worst-case expectile approach with a more conservative method based on stochastic order, which is referred to as ‘model aggregation’.

Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result

PT Aneka Tambang Tbk (ANTAM) received an award as the most sought-after stock issuer in Indonesia in 2016. That stock continued to attract investors in 2022 due to a 105% increase in net profit and a 19% increase in sales from the previous year. Despite the upward trend, investors still had doubts due to the fluctuating movement of ANTAM's stock prices. Therefore, forecasting was needed to determine the future movement of stock prices. The Backpropagation Neural Network method had good capabilities for fluctuating data types. However, this method has the disadvantage of a lengthy iteration process. To handle this limitation, The Nguyen-Widrow weighted setting was applied to address this constraint. The expected Shortfall (ES) method used the forecasting results to measure investment risk. This research uses ANTAM stock closing price data from May 2, 2018, to May 31, 2023. Based on the analysis results, the best architecture was obtained with a configuration of 5-11-1, using Nguyen-Widrow weight initialization and a combination of a learning rate of 0.5 and momentum of 0.9. This architecture yielded a prediction error based on the Mean Absolute Percentage Error (MAPE) of 1.9947%. Risk measurement with the ES method based on the prediction for the next 60 periods showed that at a 95% confidence level, the risk value was 0.002181; at a 90% confidence level, it was 0.002165; at an 85% confidence level, it was 0.002148, and at an 80% confidence level, it was 0.002132.

Novel collision risk measurement method for multi-ship encounters via velocity obstacles and temporal proximity

The collision risk assessment of multi-ship encounters in busy waters is of great significance for collision risk early warning and the intelligent supervision of maritime traffic. In this paper, a quantitative and visual evaluation method is proposed to describe changes in multi-ship collision risk in the velocity space according to multi-ship collision risk characteristics. Non-linear velocity obstacles and closest point of approach were combined to measure the risk of ship collision from the perspective of both the difficulty and urgency of the encounter. The proposed method was verified via case studies using historical Automatic Identification System data for the East China Sea. The method advantages were confirmed via comparison to the conventional time-varying collision risk and collision risk index methods. The results show that the proposed method can quantify changes in multi-ship collision risk more intuitively and effectively. The proposed method could be further developed as a new risk indicator for maritime traffic safety management and could aid in the navigation safety of manned and unmanned ships.

A New Measure of Climate Transition Risk Based on Distance to a Global Emission Factor Frontier

Targeted financing of transition to a "net zero" global economy entails climate transition risk. We propose a measure of transition risk at the country-sector dyad level composed of five tiers of transition risk based on two factors: i) the gap between a dyad's existing emission factor (EF) – a measure of the greenhouse gas intensity of output – and the global 'frontier' sectoral EF, and ii) a dyad's recent convergence towards the frontier EF. Dyads that are either close to the frontier or converging towards the frontier carry lower transition risk. Our measure, using 45 sectors across 66 countries, accounts for both direct greenhouse gas emissions as well as those that enter into production through complex supply chains as captured by intercountry, input-output tables, and can be applied at different levels of stringency to high-, middle-, and low-income economies. Our measure thus accounts for, and sheds light on, EF reductions through investment in lower emissions production techniques in own facilities as well as sourcing intermediate inputs with lower embodied emissions.

News event memory in amnestic and non-amnestic MCI, heritable risk for dementia, and subjective memory complaints

Robust and sensitive clinical measures are needed for more accurate and earlier detection of Alzheimer’s disease (AD), for staging preclinical AD, and for gauging the efficacy of treatments. Mild impairment on episodic memory tests is thought to indicate a cognitive risk of developing AD and mild cognitive impairment (MCI), considered to be a transitional stage between normal aging and AD. Novel tests of semantic memory, such as memory for news events, are also impaired early on but have received little clinical attention even though they may provide a novel way to assess cognitive risk for AD. We examined memory for news events in older individuals with normal cognition (NC, N=34), amnestic MCI (aMCI, N=27), and non-aMCI (N=10) using the novel Retrograde Memory News Events Test (RM-NET). We asked if news event memory was sensitive to 1) aMCI and also non-aMCI, which has rarely been examined, 2) genetic risk for dementia (positive family history of dementia, presence of an APOE-4 allele, or polygenic risk for AD), and 3) subjective memory functioning judgments about the past. We found that both MCI subgroups exhibited impaired RM-NET Lifespan accuracy scores together with temporally-limited retrograde amnesia. For the aMCI group amnesia extended back 45 years prior to testing, but not beyond that time frame. The extent of retrograde amnesia could not be reliably estimated in the small non-aMCI group. The effect sizes for MCI on the RM-NET were medium for the non-aMCI group and large for the aMCI group, whereas the effect sizes of participant characteristics on RM-NET accuracy scores were small. For the combined MCI group (N=37), news event memory was significantly related to positive family history of dementia but was not related to the more specific genetic markers of AD risk. For the NC group, news event memory was not related to any measure of genetic risk. Objective measures of past memory from the RM-NET were not related to subjective memory judgements about the present or the recent past in either group. By contrast, when individuals subjectively compared their present versus past memory abilities, there was a significant association between this judgment and objective measures of the past from the RM-NET (direct association for the NC group and inverse for the MCI group). The RM-NET holds significant promise for early identification of those with cognitive and genetic risk factors for AD and non-AD dementias.

Measuring DeFi risk

Decentralized finance (DeFi) lending has grown from nonexistent in 2017 to nearly $45 billion in total value locked across the three largest protocols in December 2021 before falling to roughly $15 billion in July 2023. The platforms use cryptocurrencies as collateral, matching speculative margin traders with yield-seeking depositors who lend coins pegged to the dollar (stable coins). Depositors receive claims guaranteed by a basket of collateral, similar to new stable coins. We develop a framework that requires only knowledge of total deposits and borrowings to measure the total protocol risk to lenders and borrowers. Using data from major protocols, the risk measures find that system fragility increased beyond reasonable levels around mid-2021, and we identify time periods exposing lenders to a potential collateral loss conditional on continuing extreme fluctuations in coin prices. Moreover, our results indicate that the liquidation process, a risk mitigation strategy, is not timely and is less effective for high-risk levels. Overall, the model provides two easily implementable aggregate risk metrics that capture the perspectives of synthetic investors and provide early warning signals as the industry moves from collateral-guaranteed deposits to fiat money.

Markov decision processes with risk-sensitive criteria: an overview

AbstractThe paper provides an overview of the theory and applications of risk-sensitive Markov decision processes. The term ’risk-sensitive’ refers here to the use of the Optimized Certainty Equivalent as a means to measure expectation and risk. This comprises the well-known entropic risk measure and Conditional Value-at-Risk. We restrict our considerations to stationary problems with an infinite time horizon. Conditions are given under which optimal policies exist and solution procedures are explained. We present both the theory when the Optimized Certainty Equivalent is applied recursively as well as the case where it is applied to the cumulated reward. Discounted as well as non-discounted models are reviewed.

Risk-constrained bidding and offering strategy for sector-coupled electricity-hydrogen systems incorporating accessibility level of mobility sector

To enhance the economic viability of integrated energy systems, it is important to balance risk and reward while ensuring operational flexibility and compliance with regulatory constraints. Developing an integrated risk measurement method for energy trading in energy markets that complies with regulatory constraints adds another layer of complexity, especially when dealing with large-scale heterogeneous energy systems, such as those dominated by green hydrogen. Ensuring adherence to market regulations, grid codes, and flexible requirements requires a thorough understanding of legal frameworks and policy considerations, as well as integrating regulatory constraints into the bidding/offering strategies. To address these challenges and enable the profitable development of integrated electricity-hydrogen energy systems (IEHSs), this paper develops a risk-constrained bidding/offering self-scheduling strategy for IEHSs that allows for simultaneous trading in day-ahead energy and reserve electricity markets. The developed self-scheduling strategy regulates hydrogen and power flow in a centralized manner, taking into account the operational limits of the power sector, hydrogen fueling stations (HFSs), electric vehicle parking lots, fuel cell vehicle parking lots, and renewable energy sources. As a salient feature, the important human-related factors in the mobility sector are included in the proposed strategy to motivate private owners to collaborate with IEHSs and to accommodate the charging demand of vehicles with electricity market signals. To obtain the robust day-ahead schedule, economic risks associated with renewable power generation, calls for reserve deployment, and electricity market prices are taken into consideration. Towards this end, a stochastic adaptive robust optimization approach is proposed to control the risk of profit variability, which is solved by a nested column-and-constraint generation algorithm. The effectiveness and feasibility of the proposed strategy are verified using the modified IEEE 14-bus test system, as a standard scale of local IEHSs. The numerical results highlight the synergies impact between HFSs and the power sector, resulting in an increase in the profit of the IEHS from $101.654k to $275.078k. Furthermore, the IEHS can improve its profit margin and competitiveness by employing the tri-level stochastic adaptive robust optimization approach. This approach ensures that the IEHS is resistant to sudden changes in real-time interactions, even with an 8.14% increase in the imbalance cost.